Search results for "Markov proce"
showing 10 items of 147 documents
Gibbs measures in a markovian context and dimension
2001
Exact non-Markovian dynamics of Gaussian quantum channels: Finite-time and asymptotic regimes
2018
We investigate the Markovian and non-Markovian dynamics of Gaussian quantum channels, exploiting a recently introduced necessary and sufficient criterion and the ensuing measure of non-Markovianity based on the violation of the divisibility property of the dynamical map. We compare the paradigmatic instances of Quantum Brownian motion (QBM) and Pure Damping (PD) channels, and for the former we find that the exact dynamical evolution is always non-Markovian in the finite-time as well as in the asymptotic regimes, for any nonvanishing value of the non-Markovianity parameter. If one resorts to the rotating wave approximated (RWA) form of the QBM, that neglects the anomalous diffusion contribut…
Detecting faulty wireless sensor nodes through Stochastic classification
2011
In many distributed systems, the possibility to adapt the behavior of the involved resources in response to unforeseen failures is an important requirement in order to significantly reduce the costs of management. Autonomous detection of faulty entities, however, is often a challenging task, especially when no direct human intervention is possible, as is the case for many scenarios involving Wireless Sensor Networks (WSNs), which usually operate in inaccessible and hostile environments. This paper presents an unsupervised approach for identifying faulty sensor nodes within a WSN. The proposed algorithm uses a probabilistic approach based on Markov Random Fields, requiring exclusively an ana…
Channel Assembling with Priority-Based Queues in Cognitive Radio Networks: Strategies and Performance Evaluation
2014
[EN] With the implementation of channel assembling (CA) techniques, higher data rate can be achieved for secondary users in multi-channel cognitive radio networks. Recent studies which are based on loss systems show that maximal capacity can be achieved using dynamic CA strategies. However the channel allocation schemes suffer from high blocking and forced termination when primary users become active. In this paper, we propose to introduce queues for secondary users so that those flows that would otherwise be blocked or forcibly terminated could be buffered and possibly served later. More specifically, in a multi-channel network with heterogeneous traffic, two queues are separately allocate…
Channel aggregation with guard-band in D-OFDM based CRNs: Modeling and performance evaluation
2016
Channel aggregation (CA) techniques can offer flexible channel allocation and improve overall system performance in multi-channel cognitive radio networks (CRNs). Although many CA techniques have been proposed and studied, the impact of guard-band on CA for channel access has not been addressed in-depth. In this paper, we study the guard-band allocation mechanisms in discontinuous-orthogonal frequency division multiplexing (D-OFDM) based CRNs, and investigate the impact of guard-band sharing on SU flows when CA is enabled. Continuous time Markov chain (CTMC) based models have been developed in order to investigate the stochastic behavior of PU and SU flows. Based on our mathematical analysi…
World Influence of Infectious Diseases from Wikipedia Network Analysis
2019
AbstractWe consider the network of 5 416 537 articles of English Wikipedia extracted in 2017. Using the recent reduced Google matrix (REGOMAX) method we construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix we determine the sensitivity of world countries to specific diseases integrat…
Modeling and Performance Analysis of Channel Assembling in Multichannel Cognitive Radio Networks With Spectrum Adaptation
2012
[EN] To accommodate spectrum access in multichannel cognitive radio networks (CRNs), the channel-assembling technique, which combines several channels together as one channel, has been proposed in many medium access control (MAC) protocols. However, analytical models for CRNs enabled with this technique have not been thoroughly investigated. In this paper, two representative channel-assembling strategies that consider spectrum adaptation and heterogeneous traffic are proposed, and the performance of these strategies is evaluated based on the proposed continuous-time Markov chain (CTMC) models. Moreover, approximations of these models in the quasistationary regime are analyzed, and closed-fo…
Non-equilibrium Markov state modeling of periodically driven biomolecules
2019
Molecular dynamics simulations allow to study the structure and dynamics of single biomolecules in microscopic detail. However, many processes occur on time scales beyond the reach of fully atomistic simulations and require coarse-grained multiscale models. While systematic approaches to construct such models have become available, these typically rely on microscopic dynamics that obey detailed balance. In vivo, however, biomolecules are constantly driven away from equilibrium in order to perform specific functions and thus break detailed balance. Here we introduce a method to construct Markov state models for systems that are driven through periodically changing one (or several) external p…
Remarks on IEEE 802.11 DCF performance analysis
2005
This letter presents a new approach to evaluate the throughput/delay performance of the 802.11 distributed coordination function (DCF). Our approach relies on elementary conditional probability arguments rather than bidimensional Markov chains (as proposed in previous models) and can be easily extended to account for backoff operation more general than DCF's one.
Group Metropolis Sampling
2017
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…